Traditionally, images are understood based on their primitive features such as color, texture, and shape. The proposed feature extraction methods usually cover a range of primitive features. SIFT, for example, in addition to the shape-based information, extracts texture and color information to some extent. Thus, different descriptors may cover a common range of primitive features which we call information overlap. Selecting a set of feature descriptors with low information overlap allows more comprehensive understanding of the data by providing a broader range of new features. This article introduces a new method based on information theory for comparing various descriptors. The idea is to code each description of an image by Huffman coding. The distance between the coded descriptions are then measured using Levenshtein distance as the information overlap. Results show that the computed information overlap clearly describes the differences between the learning from different descriptions of Earth Observation images.